A Physically Constrained Calibration Database for Land Surface Temperature Using Infrared Retrieval Algorithms
Abstract
:1. Introduction
2. Methodology
2.1. The Problem
2.2. Radiative Transfer Simulations
2.3. Characteristics of Atmospheric Profiles Relevant for Radiative Transfer in the TIR Window
2.4. A Calibration Database
- (1)
- Define classes of (from 200 K to 330 K in steps of 5 K) and (from 0 to 6 cm in classes of 0.75 cm—values greater than this should be treated with the coefficient corresponding to the last class).
- (2)
- Iterate in the SeeBor clear-sky profile database to fill each class in the phase space (as in Figure 2c) with one case each. When a new profile is selected, it is ensured that its great-circle distance to the already selected profiles is greater than an initial distance of 15 degrees, which guarantees a wide geographical coverage. After a sufficiently large number of tries (in this case 30,000), the distance criterion is relaxed in steps of minus 1 degree, until the whole phase space is filled.
- (3)
- For each of the previously selected profiles, assign a new based on the ranges of observed in Figure 3. The choice of the range of perturbations to apply is key to the performance of the chosen model and may depend on the region of interest. In the case of this work, a range of ±15 K around in steps of 5 K showed an overall good performance. As will be seen, large biases arise when non-physical cases are included or if the somewhat more extreme cases are not taken into account.
- (4)
- Each of these conditions may be sensed from angles ranging from 0 (nadir view) to 70° in steps of 2.5°. It is important to discretize the viewing geometry in this way because this is an intrinsically non-linear problem. The upper limit of the might be adapted for the sensor under analysis. Previous calibration exercises show that above this viewing angle limit the retrieval errors are generally too high, especially for moister atmospheres [15].
- (5)
- For the emissivity, a range of possible values are attributed to each of the cases above: values of from 0.93 to 1.0 in steps of 0.01, and then, in the case of a GSW model, it is appropriate to prescribe departures from this value for : −0.015 to 0.035 in steps of 0.01 (excluding cases where ), as suggested by Figure 4.
3. Results
3.1. Error Statistics of the Proposed Calibration Database
3.2. Sensitivity to the Distribution of Relevant Variables
4. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Database | Selection of Profiles | Number of Profiles | Prescribed Range (K) |
---|---|---|---|
Baseline: WTS_−15_15 | Full coverage of the LST/TCWV phase space | 116 | −15 to +15 |
FLAT14_−15_15 | Flat distribution of TCWV with 14 profiles per TCWV class | 112 | −15 to +15 |
FLAT10_−15_15 | Flat distribution of TCWV with 10 profiles per TCWV class | 80 | −15 to +15 |
WTS_−10_10 | Full coverage of the LST/TCWV phase space | 116 | −10 to +10 |
WTS_−10_15 | Full coverage of the LST/TCWV phase space | 116 | −10 to +15 |
WTS_−10_20 | Full coverage of the LST/TCWV phase space | 116 | −10 to +20 |
WTS_−15_20 | Full coverage of the LST/TCWV phase space | 116 | −15 to +20 |
WTS_−20_15 | Full coverage of the LST/TCWV phase space | 116 | −20 to +15 |
WTS_−20_20 | Full coverage of the LST/TCWV phase space | 116 | −20 to +20 |
WTS_−20_25 | Full coverage of the LST/TCWV phase space | 116 | −20 to +25 |
WTS_−25_25 | Full coverage of the LST/TCWV phase space | 116 | −25 to +25 |
Database | Bias (K) | RMSE (K) | Bias Stdev (K) | RMSE Stdev (K) |
---|---|---|---|---|
Baseline: WTS_−15_15 | −0.09 | 0.78 | 0.14 | 0.67 |
FLAT14_−15_15 | −0.12 | 0.81 | 0.38 | 0.70 |
FLAT10_−15_15 | −0.11 | 0.82 | 0.32 | 0.72 |
WTS_−10_10 | 0.05 | 0.74 | 0.26 | 0.64 |
WTS_−10_15 | 0.07 | 0.76 | 0.34 | 0.69 |
WTS_−10_20 | 0.09 | 0.81 | 0.41 | 0.73 |
WTS_−15_20 | −0.02 | 0.76 | 0.21 | 0.67 |
WTS_−20_15 | −0.11 | 0.79 | 0.14 | 0.68 |
WTS_−20_20 | −0.12 | 0.78 | 0.14 | 0.68 |
WTS_−20_25 | −0.11 | 0.78 | 0.15 | 0.68 |
WTS_−25_25 | −0.25 | 0.87 | 0.22 | 0.73 |
Database | Bias (K) | RMSE (K) | Bias Stdev (K) | RMSE Stdev (K) |
---|---|---|---|---|
Baseline: WTS_−15_15 | 0.09 | 2.02 | 0.71 | 1.63 |
FLAT14_−15_15 | 0.11 | 2.08 | 0.73 | 1.42 |
FLAT10_−15_15 | −0.04 | 2.05 | 0.69 | 1.38 |
WTS_−10_10 | 0.55 | 1.97 | 0.70 | 1.35 |
WTS_−10_15 | 0.76 | 2.19 | 0.92 | 1.54 |
WTS_−10_20 | 0.89 | 2.39 | 1.09 | 1.72 |
WTS_−15_20 | 0.43 | 2.28 | 0.83 | 1.69 |
WTS_−20_15 | −0.13 | 2.23 | 0.71 | 1.67 |
WTS_−20_20 | 0.04 | 2.34 | 0.76 | 1.68 |
WTS_−20_25 | 0.16 | 2.46 | 0.83 | 1.89 |
WTS_−25_25 | −0.28 | 2.67 | 0.89 | 2.07 |
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Martins, J.P.A.; Trigo, I.F.; Bento, V.A.; Da Camara, C. A Physically Constrained Calibration Database for Land Surface Temperature Using Infrared Retrieval Algorithms. Remote Sens. 2016, 8, 808. https://doi.org/10.3390/rs8100808
Martins JPA, Trigo IF, Bento VA, Da Camara C. A Physically Constrained Calibration Database for Land Surface Temperature Using Infrared Retrieval Algorithms. Remote Sensing. 2016; 8(10):808. https://doi.org/10.3390/rs8100808
Chicago/Turabian StyleMartins, João P. A., Isabel F. Trigo, Virgílio A. Bento, and Carlos Da Camara. 2016. "A Physically Constrained Calibration Database for Land Surface Temperature Using Infrared Retrieval Algorithms" Remote Sensing 8, no. 10: 808. https://doi.org/10.3390/rs8100808
APA StyleMartins, J. P. A., Trigo, I. F., Bento, V. A., & Da Camara, C. (2016). A Physically Constrained Calibration Database for Land Surface Temperature Using Infrared Retrieval Algorithms. Remote Sensing, 8(10), 808. https://doi.org/10.3390/rs8100808